Abstract / Summary
Abstract AI-enabled clinical decision support systems (CDSS) have the potential to support the detection of patient deterioration, but their value depends on outputs that fit clinical workflows, support reasoning, and minimise alert fatigue. Little is known about clinician perspectives on Multi-Output Early Warning Systems (MO-EWS), which provide simultaneous predictions (e.g., overall deterioration, organ- or diagnosis-specific). This study explores clinician views on potential future MO-EWS and offers user-centred recommendations that may inform the design, usability, and adoption. The qualitative method of reflexive thematic analysis (RTA) was used to examine clinicians’ perceptions of existing Early Warning Systems (EWS) and envisioned MO-EWS tools for predicting clinical deterioration. Between January and June 2025, 22 clinicians, 11 doctors and 11 nurses, from Australian hospitals with experience in managing clinical deterioration participated in semi-structured interviews using an interview guide. Interview transcripts were analysed to generate codes and themes. Themes were mapped to system components (user interface (UI), Artificial Intelligence (AI) model, and clinical workflow) to develop component-level recommendations and assess their feasibility. Participants found existing EWS sometimes distracting and unsafe. They envisioned AI-based systems that provide multiple, contextualised outputs (e.g., causes of deterioration, organ dysfunction, clinical severity) as layered, adaptable tools that could support reasoning, prioritisation, and communication. While many UI improvements were perceived as achievable with current technologies, we noted that model development is more complex and that workflow integration would likely depend on infrastructure, policies, and clinician preferences. Clinicians in this study perceived potential benefits in MO-EWS that provide richer information when predicting clinical deterioration in individual patients. However, we also identified several design and implementation challenges. The resulting component-level recommendations represent provisional design hypotheses that require future prototyping and rigorous contextual evaluation before their impact on clinical reasoning or system adoption can be determined.